Chun Pang Chow, Hiroyuki Kasahara, Yoichi Sugita
arXiv 2 Mar 2026 · Econometrics
arXiv:2603.01492 · PDF · DOI · OpenAlex · Extracted main text
We establish nonparametric identification of production functions, total factor productivity (TFP), price markups, and firms' output prices and quantities, as well as consumer demand, using firm-level revenue data, without observing output quantity, in a monopolistically competitive environment with a fully nonparametric demand system. This result overturns the widely held view -- formalized by Bond, Hashemi, Kaplan, and Zoch (2021) -- that output elasticities and markups are not nonparametrically identifiable from revenue data without quantity information. Under the additional restriction that demand satisfies the homothetic single-aggregator (HSA) structure of Matsuyama and Ushchev (2017), we further nonparametrically identify the representative consumer's utility function from firm-level revenue data. This new identification result enables counterfactual welfare analysis without parametric assumptions on preferences. We propose a semiparametric estimator that is feasible for standard firm-level datasets under a Cobb--Douglas production specification. Monte Carlo simulations show that the estimator performs well, while treating revenue as output induces substantial bias. Applying the estimator to Chilean manufacturing data, we reject the CES specification in favor of HSA, and find that market power reduces welfare by approximately 3%--6% of industry revenue in the three largest manufacturing industries in 1996.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gandhi, A., Navarro, S., and Rivers, D. A (2020) On the identification of gross output production functions | 1.000 | 8 | 4 | 100% |
| 2 | Levinsohn, J. and Petrin, A (2003) Estimating Production Functions Using Inputs to Control for Unobservables | 1.000 | 5 | 3 | 100% |
| 3 | Marschak, J. and Andrews, W (1944) Random Simultaneous Equations and the Theory of Production | 1.000 | 5 | 3 | 100% |
| 4 | Ackerberg, D. A., Caves, K., and Frazer, G (2015) Identification Properties of Recent Production Function Estimators | 0.928 | 10 | 5 | 80% |
| 5 | Olley, G. S. and Pakes, A (1996) The Dynamics of Productivity in the Telecommunications Equipment Industry | 0.928 | 4 | 3 | 100% |
| 6 | Matsuyama, K. and Ushchev, P (2017) Beyond CES: Three Alternative Classes of Flexible Homothetic Demand Systems, CEPR Discussion Papers DP12210 | 0.899 | 11 | 5 | 73% |
| 7 | Chiappori, P.-A., Komunjer, I., and Kristensen, D (2015) Nonparametric Identification and Estimation of Transformation Models | 0.874 | 9 | 2 | 100% |
| 8 | De Loecker, J (2011) Product Differentiation, Multiproduct Firms, and Estimating the Impact of Trade Liberalization on Productivity | 0.874 | 5 | 2 | 100% |
| 9 | Doraszelski, U. and Jaumandreu, J (2018) Measuring the Bias of Technological Change | 0.874 | 5 | 2 | 100% |
| 10 | Firpo, S., Galvao, A. F., Pinto, C., Poirier, A., and Sanroman, G (2022) GMM quantile regression | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 58 scored citations.